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Dear Editor:

We read with interest the Opinion piece entitled “An agenda for personalized medicine” in the October 8, 2009 edition of Nature. Our two companies, though commercially distinct with differentiated products, would like to respond to this piece jointly to show our commitment to working together in an open, transparent fashion.

Our companies agree with most of the recommendations Ng and colleagues made.  Without doubt, genotype-based risk prediction for common, multifactorial diseases is still in its infancy.  More work must be done to standardize markers used; to better explain the contribution of genetics to common, complex diseases; and to incorporate common genetic variants into clinical practice.  Each company, however, has a few points of disagreement and/or explanation it feels important to articulate.  These points from each company follow.

Response by Navigenics:

With regard to the specific recommendations, Navigenics agrees with most of the suggestions.  For example, we agree with the authors that results showing less than average risk should not be a primary point of focus, a viewpoint that has been incorporated into our service offering in a variety of ways.  Ng et al. recommend “that DTC companies report the proportion of the genetic contribution of a disease that can be attributed to the markers used in their test…” There are many metrics that can be used to describe the completeness/accuracy of these types of tests. However, each of them has advantages and disadvantages, and all can be misinterpreted by experts and laypersons alike.  Furthermore, the call for such information must be put in context with currently implemented non-genetic risk communication. For example, does your doctor know/communicate what percentage of the total risk of cardiac disease is contributed by your cholesterol level?  Or your family history?  Clearly, risk communication has, and will continue to be, an important area of research for the community.

We agree that associations must be replicated in other ethnicities; that prospective studies will be helpful in further assessing the validity of predictions; and that sequencing should be used when the technology becomes more affordable.  The monitoring of behavioral outcomes is another important avenue for future research, and to this end, Navigenics is collaborating with the Scripps Translational Science Institute on a 20-year longitudinal outcomes study.  Pharmacogenomic markers may also offer immediate value to individuals.

We also agree on the importance of including the same strong-effect markers, and with a few exceptions, our companies are consistent.  A standard set of markers would be valuable to the industry and personalized medicine in general, and it may be most practical for a third party to assess clinical validity.  The catalog of genome-wide association loci sponsored by the National Human Genome Research Institute is an example of such a resource.  Further public-private efforts could be placed into grading the cumulative evidence supporting various marker-disease associations using, for example, the Venice criteria.

Regarding the use of surrogate risk markers, Navigenics initially used markers in linkage disequilibrium with published SNPs (with a requirement of r² = 1) to tag SNPs that were not on its genotyping platform. However, Navigenics now directly targets published SNPs, except for a few loci in the HLA region.

Response by 23andMe:

We would like to discuss two technical points about the article.  The authors presented these points in a relatively balanced manner but the subtleties have led to misinterpretations in subsequent media coverage.

The first point is that in the comparison of risk estimates, the thresholds for what Ng and colleagues consider to be “average risk” (0.95 ≤ relative risk ≤ 1.05) are somewhat restrictive.  Using this definition would be akin to telling an individual that her risk of a condition was “increased” because it was 5% more than average—technically true, but unlikely to be meaningful.  While there is no scientific consensus on what magnitude of risk equates with “meaningful” increased risk, one typically does not find relative risks less than 1.5 used today in clinical practice (e.g. family history, non-genetic biomarkers, environmental risk factors).  Thus, it would have been more appropriate to perform the consistency comparison using a wider window for “average risk”.

The second point concerns measures of genetic contribution to disease.  Ng and colleagues correctly note that for some diseases, less than half of the genetic contribution can be explained by known markers, which may lead to false negatives and false positives.  However, the metrics Ng and colleagues use to assess genetic contribution―heritability and proportion of genetic contribution explained―are population-level measures that should not be applied to individual-level risk estimates, as illustrated by two examples.

Estimates of the genetic component (heritability) of Parkinson’s disease range from 0% to 27%, meaning that genetics explains at most 27% of the variance in disease risk.  However, the G2019S mutation in the LRRK2 gene confers a 40% to 60% lifetime risk of developing Parkinson’s disease, compared to 1% to 2% on average.  The low heritability cannot—and should not—be interpreted to mean that the risk estimate of 40% to 60% is inaccurate or irrelevant, or that relative risk due to genetics must be small.  (Visscher et al. address common misconceptions about heritability in a 2009 review.)

Ng and colleagues also note that the contribution of known genetic markers to disease can be measured using percent variance explained, another population-level measure.  This statistic, too, can be misleading if applied at an individual level.  As an example, risk data for BRCA1/2 mutations and breast cancer in Ashkenazi Jews can be entered into a liability threshold model, which has been used to estimate percent variance explained by markers discovered in genome-wide association studies.  According to the model, the three BRCA1/2 mutations most commonly seen in Ashkenazi Jewish populations only account for about 2.5% of the variance in breast cancer risk in that population (Table 1), primarily because of the rarity of the mutations.

It would be misleading to advise a woman receiving a positive result for the 5382insC mutation in BRCA1 not to take the 81% lifetime risk of breast cancer seriously just because that mutation explains only 1.1% of the variance in breast cancer risk.  Though we agree that showing the percent variance explained by reported markers could indicate the state of genetic research on a phenotype, a low value does not necessarily mean that individual-level estimates of risk are unreliable or should be disregarded.

[Table 1.  Percent variance of breast cancer risk explained by BRCA1/2 mutations in Ashkenazi Jews, calculated using model in Raychaudhuri et al. (2008).  All data (except percent variance explained) are from Fodor et al. (1998).]

In closing, both of our companies thank Ng and colleagues for their serious consideration of genomics and personalized medicine.  We welcome further dialogue on how best to improve our offerings to the public.

Sincerely,

23andMe, Inc.

Navigenics, Inc.

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Dan Vorhaus at Genomics Law Report has launched “What ELSI is new?”, a series of guest posts on the most pressing ethical, legal, and social issues (ELSI) relating to genomics.  (You gotta love the name.)  The contributor list is a who’s who of voices in the ELSI, policy, and blog worlds.  I’m honored to be representing 23andMe in the discussion; my own post will appear next Tuesday.

Today’s post is by Prof. Hank Greely, with whom I had the pleasure of serving on a panel at the Stanford Genetics departmental retreat a few weeks ago.  He makes the important point that trained genetics professionals are too few and far between to expect consultations for every genomics consumer, especially when sequence information is getting cheaper and cheaper.  What’s the right response: to restrict access to genetic information because of the danger of misinterpretation, or to accept the risk but mitigate it as well as possible through education?

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We also met Grant Wood of Intermountain Healthcare, based in Salt Lake City, Utah. From his point of view as a health IT strategist, GINA’s protections make it easier for genetic data to be integrated with electronic health records (EHRs), which Intermountain maintains for about 55% of Utah’s residents. Intermountain Healthcare recently won a grant from Microsoft to develop tools for adding family health history to Microsoft HealthVault. We talked about how useful it would be to add data from genome-wide scans to EHRs, which include a wide range of clinical data in addition to family history. Patients and clinicians would benefit from knowing the genetic component of risk, and researchers could perform genetic association studies against a much larger range of phenotype data than has been studied before.

The day ended with a screening of the deeply moving documentary In the Family. The film centers on Joanna Rudnick, who wrote, produced, and directed the film. Learning of her family history of breast and ovarian cancer, she gets tested for mutations in BRCA1 and BRCA2, and tests positive for a mutation that gives an estimated 80-90% lifetime risk of breast cancer and a 40-60% lifetime risk of ovarian cancer. (23andMe does not provide information on breast cancer mutations in the BRCA1 or BRCA2 genes.)

During her journey, Rudnick interviews a number of very brave women to see how they deal with their own discovery that they carry a potentially lethal genetic payload: whether and when to have ovaries or breasts removed, how to share information with relatives, the guilt of “passing on” a mutated gene.

Rudnick not only captures her emotions and the effect of what she learns on her relationship with her significant other and her family, but brings several other dimensions into the discussion on genetic testing. We see how a teenage girl reacts to the possibility that she might also carry a BRCA1 mutation. We also see how men—who can also get breast cancer, but more importantly can pass BRCA1/2 mutations to their daughters—confront their genotypes (over pints of beer, of course).

I was particularly struck by Rudnick’s exploration of the attitude of some African Americans toward testing and participation in clinical research. Historical memory of the Tuskegee experiments and racial discrimination in general has led some African Americans to be suspicious of researchers’ intents. Poverty, which disproportionately affects African Americans, also has an effect on whether one wants to get tested. To paraphrase one astute woman: “Enough stuff already happens to black people. Black people don’t go looking for more stuff.”

In the Family will come to PBS on P.O.V. on October 1, 2008. I highly recommend it.

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The Genetic Alliance was founded in 1986 as an umbrella organization for different disease advocacy groups. Today, Genetic Alliance focuses on five areas of public policy:

• Genetic discrimination
• Genetic testing
• Open access to NIH-funded research
• Newborn screening
• Stem cell research

23andMe is closely aligned with Genetic Alliance on these issues—but most especially the first two. At 23andMe, we believe that individuals have a right to keep their genetic data private; so we supported the Alliance’s advocacy of the Genetic Information Non-Discrimination Act (GINA), which protects U.S. citizens against insurance or employer discrimination based on genetic data. Thanks in part to Genetic Alliance’s efforts, GINA was passed and signed into law last April. On Thursday, 23andMe co-founder Linda Avey and I will participate in the Alliance’s Genetics Day on the Hill 2008, where we will continue to discuss important science and policy issues with legislators and their staff.

We at 23andMe also believe that individuals have a right to high-quality genetic data and their interpretation. Because of the recent flood of genome-wide association studies, regulatory bodies are finding challenges in dealing with the new types of genetic information increasingly available to consumers. One of our goals is to work with various stakeholder groups to reach a consensus on how to address these challenges. On July 13, 23andMe’s Senior Director of Research Joanna Mountain will represent 23andMe in a panel called “Direct to Consumer Genetic Testing: Revolution or Risk?”

So keep an eye on this space if you want a peek into the intersection of science and policy. I’ll be attending and blogging about several interesting workshops.

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Despite the success of genome-wide association studies, many experts rightly point out the need to remember that most common diseases have a significant environmental component. For example, cigarette smoking increases one’s risk of lung cancer 10- to 20-fold, according to the National Cancer Institute. But other studies have found that even after controlling for smoking, having first-degree relatives with lung cancer increases one’s own risk by a modest amount, suggesting at least a small genetic component.

A trio of papers published today in Nature and Nature Genetics has found a SNP on chromosome 15 that is linked to lung cancer risk. Two of the studies, by Hung et al. and Amos et al., matched lung cancer cases and healthy controls for smoking behavior and compared their genotypes across 300,000 SNPs. Both studies found that one copy of the A version of rs1051730 increased subjects’ odds of lung cancer by about 1.3 times compared to those with none; having two copies increased subjects’ odds by 1.8 times. (23andMe users can see their genotype at rs1051730 in the Genome Explorer (now called Browse Raw Data).)

A 1.8-fold increase may not seem like much compared to the 10- to 20-fold increase seen for smokers. But if this genetic effect is independent of the effect of smoking, smokers with the AA genotype might find themselves at 18- to 36-fold increased risk compared to nonsmokers with the GG genotype.

A third study questions whether the SNP’s effect on lung cancer risk is truly independent of smoking. Thorgeirsson et al. found evidence that the same SNP, rs1051730, is actually linked to nicotine dependence. On average, each copy of the A version increased the number of cigarettes subjects smoked by about one cigarette per day. The authors also reported that most of the increased risk of lung cancer conferred by the A version was due to its effect on smoking. Thus, an environmental component itself appears to have a genetic component.

However, the effect of rs1051730 on smoking quantity was seen only if a subject smoked at all. Genotype at rs1051730 wasn’t connected with whether subjects smoke, only with how much they smoke once they start.

So in the nature vs. nurture debate, perhaps there is room for free will after all.

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Following on the heels of last month’s New England Journal of Medicine study on genetic associations with prostate cancer risk are three new papers in Nature Genetics. The new batch not only replicates earlier discoveries but adds at least 16 potential new associations to the mix. While there are too many to list here individually, a few are worthy of particular mention. (Links to 23andMe’s Genome Explorer (now called Browse Raw Data) are provided for each.)

Iceland’s deCODE Genetics compared the genotypes of 10,000 men diagnosed with prostate cancer with those of 29,000 healthy controls. Instead of conducting a genome-wide association study (GWAS) of hundreds of thousands of single nucleotide polymorphisms (SNPs), the authors chose to follow up on two promising candidates identified in previous GWAS. In the current study, both showed associations to prostate cancer risk.

One of the candidates, rs5945572, is located on the X chromosome. Men only have one X chromosome and thus one copy of rs5945572. Those who had the “A” version of the SNP had slightly higher odds of prostate cancer.

The fact that the SNP is located on the X chromosome—which sons inherit only from their mothers—may help to explain why brothers of men with prostate cancer are at greater risk of being affected than fathers are. If a father has the protective “G” version of the SNP on his X chromosome, he cannot pass it on to his sons; the contribution of this SNP to a man’s risk of prostate cancer is determined by which version his mother passes to him.

The second SNP identified in the deCODE study was rs721048, on chromosome 2. This SNP was analagous to rs2710646, which is in 23andMe’s Genome Explorer (Browse Raw Data). Each copy of the “A” version of rs2710646 slightly raised men’s odds of prostate cancer.

The other two studies each examined 5,000 cases and 5,000 controls. While the studies found a number of significant associations, both independently identified a link between rs10993994 and prostate cancer risk. Each copy of the “T” version of rs10993994 raised men’s odds of prostate cancer. This SNP is located in a gene called MSMB, which encodes a prostate-specific protein thought to be a biomarker for advanced forms of prostate cancer. The authors speculate that the different versions of this SNP might affect levels of the MSMB protein, but this will have to be shown in future experiments.

Further replication studies will have to be performed before all of these associations are fully accepted by the scientific community. But because rs10993994 has already been identified in two large studies, we’ll get it into the Gene Journal article (now called Health and Traits) on prostate cancer as soon as possible. Check back soon to see how this SNP fits into the picture!

(2/12/08 update: added PubMed links to study abstracts. -Andro Hsu)

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There are two simple ways to train animals to perform a new behavior: the carrot and the stick. The carrot rewards desired, “correct” behaviors, while the stick punishes “errors.” Over time, this scheme results in preference for positively reinforced behaviors, and avoidance of negatively reinforced ones. In the December 7 issue of Science, German scientists reported that the SNP rs1800497 is associated with how well subjects learn to avoid errors in a simple reward/punishment scheme. (What the paper calls the “A1-allele” of the “DRD2-TAQ-IA polymorphism” is actually what we store as the A version of the rs1800497 SNP, according to OMIM and and MutDB.)

Though we do not yet consider this finding solid enough to include in the Gene Journal (now called Health and Traits), 23andMe users can still examine their genotype at the rs1800497 using the Genome Explorer (now called Browse Raw Data). In the study, people with the GG genotype appeared to avoid choices for which they had received negative feedback, while those with the AG or AA genotypes did not seem to avoid those choices.

(There are currently no genetic associations for whether people prefer to be rewarded with actual carrots, although someone is clearly thinking about this question.)

Caveats

1) Study size. The authors only tested 12 subjects with the GG or AG genotype and 14 subjects with the AA genotype. This makes it more likely that the result could be a fluke (though the authors also present functional data to support their conclusions). At the very least, the training/testing portion of the study should be repeated in a much larger group. The same goes for other DRD2 association studies. 2) Real-world relevance. The test was highly abstract and the reinforcement simplistic and binary—this type of learning may not be relevant to real-world situations. 3) Multiple hypothesis testing. Other researchers have looked at whether this SNP is associated with behavioral traits from alcohol dependence to creativity, usually in undersized studies. If you run enough tests, something will eventually look like a positive hit just by chance.

(After the jump: detailed methods and smiley/scary faces.)

You Have Chosen…Poorly

How do you find out how well people learn? Give them a test where they have no idea what the correct answers are—and then reward or punish their choices!

During the training phase of this experiment, subjects took a series of trials. In each trial, they were presented with a pair of abstract symbols and asked to choose one. The subjects received immediate feedback about their choices: either a smiling face as a positive reinforcement (reward), or a frowning face as negative reinforcement (punishment).

The trials were repeated many times, using three different training pairs comprising six symbols—AB, CD, and EF. Of the possible choices, the most-rewarded choice was “A”, which received a happy face 80% of the time and a frowning face 20% of the time. The least-rewarded choice (and thus most punished) was “B”, which received a happy face only 20% of the time, and a frowning face 80% of the time. The reward schedule and the actual symbols used are in the figure to the right.

In the second phase, subjects were again asked to choose between two symbols. Although the same six symbols were used, in this phase the pairs did not include the three from the training set (i.e. BE and FD were shown, but not AB). The researchers measured preference for reward by recording the number of times the subjects chose “A” when it was offered. They measured avoidance of punishment by recording the number of times the subjects chose the non-”B” symbol when “B” was offered.

Those with the GG genotype at this SNP—avoiders—chose the non-”B” symbol 70% of the time. But those with the AA or AG genotypes—non-avoiders—chose the non-”B” symbol only 50% of the time, which is no different from choosing at random. The difference between avoiders and non-avoiders was of moderate statistical significance.Interestingly, there was no statistically significant difference between the two groups on preference for “A”—only for avoiding “B”.

Mechanism

The SNP occurs inside a gene called ANKK1, but scientists believe that the SNP acts on a neighboring gene, DRD2. The DRD2 gene encodes a receptor in the brain that responds to the neurotransmitter dopamine. At least two previous studies have shown that people with the AG and AA genotypes have lower levels of the dopamine D2 receptor in their brains. It’s possible that variation at the SNP affects how DRD2 is turned on and off, or that it is tightly linked to another SNP that does.

Klein et al. (2007) also used fMRI images of the brain to show that avoiders and non-avoiders had different activity levels in a zone of the brain thought to be involved in learning from errors. They suggest that “reduced dopamine D2 receptor density is associated with reduced capacity to learn negative characteristics of a stimulus from negative feedback.” In other words, if it’s true that people with the AG or AA genotypes make less of the dopamine D2 receptor, this might explain the reduction in activity they observed.

The authors also cite studies showing that lower dopamine D2 receptor density is linked to addiction, obesity, and novelty-seeking behaviors such as compulsive gambling, which suggests that avoiders in this study might also be prone to these behaviors. Interestingly, another study suggests that people with the same rs1800497 genotype as the non-avoiders (AA or AG) have higher verbal creativity.

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Most people who have resolved to give up the bottle after a bout of New Year’s Eve overindulgence went cold turkey for a few weeks with no ill effects (even if by now they have gone back to their old habits). But for some people who are alcohol-dependent, quitting booze in the first place can result in severe symptoms, such as withdrawal seizures and delirium tremens. A French study in the January issue of Alcoholism: Clinical and Experimental Research found evidence that two SNPs in a single gene were linked with how likely alcohol-dependent people were to have withdrawal seizures. Because this finding is of modest statistical significance, and because it only affects a small fraction of people (about 3% of alcohol-dependent people), we do not currently include this report in the Gene Journal (now called Health and Traits).

However, 23andMe users can look at their genotypes in the Genome Explorer (now called Browse Raw Data). The two SNPs identified in the study are rs27072 and rs27048. The study found that alcohol-dependent people with the CC genotype at either SNP have just under twice the odds of having withdrawal seizures as those with CT or TT.

Caveats

1) Small study size. As with many studies of behavioral traits, small sample sizes can lead to evidence for a conclusion that are actually statistical flukes. 2) Multiple hypothesis testing. The authors performed at least three types of analyses on eight markers. There is reason to wonder if these results were due to a fluke—run enough tests and you’ll eventually get a hit. However, seeing statistical significance in more than one type of analysis, along with previous studies showing association between withdrawal symptoms and the DAT1 gene (though not with the SNPs reported here), lend support to the hypothesis that there is a real association, albeit possibly a small one.

(After the jump: detailed background and a dated cultural reference.)

Quitting the Sauce

Alcohol dependence is thought to be heritable because it clusters in families. Yet there have not been any conclusive links between the disease and genetic markers. Some of this lack of progress is because past studies have suffered from statistical biases and small sample size. For a rather extreme example of the problem with the latter, think about flipping a coin twice and coming up with heads twice. Would you be justified in concluding that the coin is double-headed? (The answer is no.)

Another issue is that alcohol dependence is a heterogeneous disorder whose definition is in flux. Most people have heard the term alcoholism, which the American Medical Association defines as “a primary, chronic disease characterized by impaired control over drinking, preoccupation with the drug alcohol, use of alcohol despite adverse consequences, and distortions in thinking.”

But the psychiatrists’ bible, the DSM-IV, recently split its diagnoses into alcohol abuse and alcohol dependence. The former is defined as repeated drinking despite negative consequences (for example, on work or relationships), while the latter is defined as alcohol abuse in addition to tolerance and withdrawal: the classic signs of addiction. However, not all withdrawal symptoms—cravings, hallucinations, the shakes, seizures, and delirium tremens—need to occur to justify a diagnosis of alcohol dependence. Furthermore, the nature of addiction itself could be heterogeneous—physical, psychological, or both—meaning that the environmental and biochemical (and thus potentially genetic) events that lead to alcohol dependence can differ from person to person.

For example, take people who wear watches. Some do because they are obsessive about being on time, others do because they like stylish accessories, while a third group might do it to cover up the tan line on their wrist. Add to this heterogeneity an expansion of the category of “wearing a watch” to “knowing what time it is”—which could include carrying a pocketwatch, a cell phone, or standing next to Flavor Flav—and it becomes a nightmare to find a common factor.

Le Strat et al. (2008) try to reduce heterogeneity by looking for a genetic link to a specific symptom in alcohol-dependent patients recruited from French treatment clinics—in our example, whittling down the definition to just those people who wear digital watches. Withdrawal seizures are a potentially life-threatening side effect that occur when someone who is alcohol dependent quits drinking. Yet not all alcohol dependents have seizures. Withdrawal seizures are thus a sufficient, but not necessary, symptom of alcohol dependence.

The authors examined a single candidate gene, DAT1, based on previous (though inconclusive) evidence that different DAT1 variations are linked to alcohol dependence and withdrawal symptoms. DAT1 encodes a protein that plays a role in the signaling of the neurotransmitter dopamine. Dopamine receives a lot of attention in this field because of its well-accepted role in rewarding behavior. In particular, mice engineered to lack the DAT1 gene avoid alcohol, although it is not clear whether dopamine is playing a role in rewarding alcohol intake, the negative effects of withdrawal, or both.

Three different types of analyses were performed for each SNP (and six other markers), and the authors found that having two copies of the C version of the SNPs rs27072 and rs27048 increased one’s chance of having withdrawal seizures. The SNPs mentioned here reached statistical significance in two of the analyses performed, but only moderately.

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One of the much-heralded claims of the post-genomic era is that personal genetic information may allow medical treatments to be tailored to an individual’s genetic makeup. While we are not there yet, a trio of new studies on heart disease may just have brought us one step closer. The studies were the product of a collaboration between researchers at Harvard Medical School and Celera (the company that helped spur completion of the public Human Genome Project).

In the first study of over 25,000 women, those with the GG or AG genotypes at the SNP rs20455 were found to have a slightly increased chance of having a heart attack—about 34% higher than those with the AA genotype. (This study confirmed at least two other preliminary findings that this SNP, which is in the gene KIF6, is associated with heart disease.)

A second study of about 3,000 subjects further confirmed the first finding, and also examined whether the SNP was associated with the outcome of treatment with the cholesterol-lowering drug pravastatin. As other studies have reported, those with the GG or AG genotypes were found to be at higher risk of having a heart attack than those with the AA genotype. But when treated with pravastatin, the risk of heart attack for people with GG or AG was substantially reduced—approximately equal to that of the lower-risk AA group. (Pravastatin treatment had little or no effect on those with the AA genotype.)

Lastly, a third study of about 4,000 subjects compared how rs20455 affected differences in outcomes for people given the standard-dose pravastatin therapy versus high-dose treatment with atorvastatin, another anti-cholesterol drug. Those with the AA genotype saw no additional benefit of receiving the more intense atorvastatin treatment compared to the standard pravastatin treatment. But people in the study with GG or AG did see a significant reduction in their risk of having a heart attack, cutting their risk by about 40% over two years.

23andMe customers may examine their genotype at rs20455 in the Genome Explorer (now called Browse Raw Data) and compare their genetic data to that of the studies in this article. Keep in mind, however, the points made in the introduction to this SNPwatch article.

In addition to the SNPwatch reminder, remember that genetics are only one factor that physicians may consider when prescribing preventive medications for heart disease or any condition. If you have concerns or questions about what you learn through 23andMe, you should contact your physician or other appropriate professional.

Update [2008/01/24]: Although our Gene Journal article on heart attacks (now called Health and Traits) does not currently cover this SNP, we believe that this association has been replicated sufficiently enough to include in the article–we’ll get to it as soon as we can!

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SSRIs are a family of commonly prescribed antidepressants. They include Celexa, Paxil, Prozac and Zoloft, and they act by influencing the activity of the neurotransmitter serotonin in the brain. Effective doses vary from person to person, and some researchers think that an individual’s genotype may affect how much or little of a drug a person needs to achieve a given effect.

In particular, the cytochrome P450 (CYP450) enzymes in the liver break down drugs in the bloodstream. That’s why people have to take drugs on a regular basis. But genetic variation in some CYP450 genes can lead to higher or lower enzyme activity, thus affecting the amount of drug circulating in the bloodstream. For this reason, doctors often have to adjust drug dosages to achieve a desired target range or clinical effect. The study of how genetic variation affects drug metabolism is known as pharmacogenomics.

A well-known pharmacogenomic effect is due to variation in the CYP2D6 gene. Most people respond to the painkiller codeine. But some individuals have a CYP2D6 genotype that results in insensitivity to the drug, while for others, codeine can have a noxious effect at dosages effective for the average person.

However, there have been far fewer pharmacogenomic studies on CYP450 variation and metabolism of SSRIs. The panel was concerned that diagnostic tests such as Roche’s AmpliChip CYP450 are being used without having been fully accepted by the biomedical community as useful. The authors thus set out to examine published studies on the AmpliChip test with respect to three standards of usefulness: technical accuracy, clinical validity, and clinical utility.

The group found that the AmpliChip product measures genotype well; it is technically accurate. But the group also found that the currently available crop of studies has not conclusively shown that variations in genotype affect SSRI levels in the bloodstream. Clinical validity is thus inconclusive. And to date, there have been no studies on clinical utility—that is, whether knowing CYP450 genotype leads to positive final outcomes such as improved patient quality of life or reduced treatment costs.

So it looks like a lot more research must be performed on the pharmacogenomics of SSRI dosage—not to mention a host of other health-related genetic associations—before we enter the age of personalized medicine.

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